Visual Representation and Stereotypes in News Media - Elliott Ash; Ruben Durante; Maria Grebenshchikova; Carlo Schwarz (Center for Economic Studies and ifo Institute (CESifo), 2022)
We propose a new method for measuring gender and ethnic stereotypes in news reports. By combining computer vision and natural language processing tools, the method allows us to analyze both images and text as well as the interaction between the two. We apply this approach to over 2 million web articles published in the New York Times and Fox News between 2000 and 2020. We find that in both outlets, men and whites are generally over-represented relative to their population share, while women and Hispanics are under-represented. We also document that news content perpetuates common stereotypes such as associating Blacks and Hispanics with low-skill jobs, crime, and poverty, and Asians with high-skill jobs and science. For jobs, we show that the relationship between visual representation and racial stereotypes holds even after controlling for the actual share of a group in a given occupation. Finally, we find that group representation in the news is influenced by the gender and ethnic identity of authors and editors.
Keywords: L82; J15; J16; Z10; C45; 330; stereotypes; gender; race; media; computer vision; text analysis